1 . Field of Invention
The present invention relates to a method and apparatus for classifying structural anomalies by characterizing the shape changes via their bounding surfaces and comparing the shape changes with shape measures representing known populations.
2 . Background Art
Detection of subtle changes in shape of structures can often times be of critical importance for detecting anomalies that are potential indicators of defect or disease. Minor shape changes of structures can often have major catastrophic results. For example, deformation in building construction components may lead to a significant decrease in structural integrity of the entire construction. Also, for example, minor deformations in high resolution, complex machined parts may lead to incompatibility with other machined parts and ultimately to the degradation of the machined device""s performance. Additionally, alterations of the shape of human anatomical organs may, for example, be an early warning sign of disease such as cancer. In each of these case, early detection of shape anomalies may be of critical importance.
Often diseases which affect human anatomical organs alter the shape of the diseased organ. For example, cancer mutates organ cells to create abnormal tumor growths. In addition, recent discoveries have revealed that diseased brains suffering from schizophrenia display abnormal shapes and growths. Early detection of these abnormal shapes and growths are of great benefit to the patient and physician in order to begin treatment as soon as possible.
Presently, diagnosis of many diseases requires painful, invasive testing. For example, a biopsy of suspected diseased tissue is often required to determine if a disease exists within the human body. While non-invasive imaging techniques such as MRI, CT and Ultrasound exist and provide valuable information regarding injury, such techniques provide little to no assistance in directly quantifying structural changes used in the diagnosis of disease.
Conventional methods for characterizing human brain disease involves no automation for characterizing shape change associated with bounded volumes and their connected surfaces. The most common approach to defining human brain disease is the comparison of the total volumes of selected brain substructures. Volume determinations are usually made using manual techniques of outlining or counting stereological points around and within the selected volume. This approach can only detect the presence of human brain disease associated with differences in total brain substructure volume and where the magnitude of such differences exceeds the degree of error associated with making manual measurements.
Many human brain diseases, such as Alzheimer""s disease, Parkinson""s disease, schizophrenia, as well as attention deficit and learning disorders of childhood are known to be associated with small brain volume differences and therefore cannot be detected using manual methods. Accurate automated methods of generating volume measures and global scale transformations provide a methodology for accurate representation of disease based on scale and volume representation. Since abnormalities of neuronal architecture have been associated with these same diseases, it is likely that the innovations described herein will discriminate individuals with these diseases from normal individuals, and allow treatment to begin. In addition, diseases associated with asymmetry can be examined using the advances described herein as well.
Shape characterization is a method which allows a user to determine abnormalities and changes in shape of structures. Such shape characterization has a multitude of practical applications. For example, shape characterization techniques may provide a means to detect shape changes in human anatomical organs. Also, for example, shape characterization may provide a means to achieve quality control in automated inspections of structural elements.
Shape characterization of human anatomical organs may provide a non-invasive manner in diagnosing abnormal growths, diseases and potential health risks. Cancerous organs with abnormal shapes might be quickly identified without the need for invasive diagnostic techniques. Automated shape characterization of the brain can detect subtle changes in brain volume not presently possible by conventional methods.
Gaussian Random Fields on Sub-Manifolds for Characterizing Brain Surfaces, Sarang C. Joshi et al., incorporated herein by reference, discloses a method of using computer hardware and computer software for automated characterization of human brain substructures and the associated disease. Specific diseases have been studied and the alterations of shape on human organs have been observed and classified. By image comparison with known classified diseased organ shapes, early, non-invasive, detection of diseases may be achieved.
One disadvantage of conventional methods is the inability to detect the critical subtle changes in human organs such as the brain. Another disadvantage of conventional diagnostic methods of human organs is that they require invasive techniques. As early detection and non-invasive methods are goals of disease diagnosis, it would be desirable to provide a means and method for disease diagnosis through the use of shape characterization. While the preferred embodiment of this invention is in the characterization of human anatomy, it should be noted that the present invention is not limited to biological application. In addition, characterizing shapes of non-biological structures such as automated inspection of parts for quality control, for example, may be accomplished.
The present invention overcomes the above-described disadvantages, and provides a method and apparatus for automated shape characterization. The present invention also provides a method and apparatus for automated shape characterization that allows for the detection of disease by means of non-invasive imaging techniques. Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims.
According to the present invention, to achieve these and other advantages and in accordance with the purpose of the invention, as embodied and broadly described herein, a method and apparatus consistent with the present invention automatically classifies shapes, for example, automatically classifying populations via shape characterizations of sub-manifolds of points, curves, surfaces and sub-volumes. Where a population is a group of individuals exhibiting a common characteristic, also referred to as a class. A method consistent with the present invention includes the steps of: 1) acquiring images of structures using conventional techniques and from the acquired image, creating a complete map based on a transform function; 2) creating a database of known shape and volume changes associated with known classification, e.g. disease populations and classifying the images based on classification characteristics; 3) characterizing the shape of a population by creating a mean composite sub-volume and mean composite bounding surface by averaging the transform maps of images classified together in the same population; 4) determining a variation around the mean composite sub-volume and bounding surfaces using the family of transform maps; and 5) determining the probability that a new structure under study belongs in a particular population based on its shape.
A method consistent with the present invention includes creating a database of known normal and diseased states of a particular structure, comparing the image of a structure under study with the images of the database and using a probability distribution function to determine whether the image under study is closer to a known anomalous image or to a normal state image. The method makes this determination based on the results of the probability distribution function which calculates the difference between a target and a template image and the probability of a match.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.